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Approach to Part using Deformable Part Model in Pedestrian Detection System
- Choi, Hye Ji;
- Shin, Nara;
- Choi, Kwang Nam
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0초록
Histogram of Oriented Gradient (HOG) proposed by Dalal and Triggs is currently the most basic algorithm to detection pedestrian. The algorithm is weak to occlusion, since the algorithm trained by the image of pedestrian full body images as one feature. As a result, the detection rate using HOG feature becomes decreases remarkably. To solve this problem, the paper proposed detection system using Deformable Part-based Model (DPM) just divided two parts of pedestrian data through latent Support Vector Machine (SVM) based machine learning. Experimental results show that proposed approach achieves better performance on detection with high accuracy than existed method [1].
키워드
Pedestrian Detection; Deformable Part Model; Histogram of Oriented Gradients; Object Detection
- 제목
- Approach to Part using Deformable Part Model in Pedestrian Detection System
- 저자
- Choi, Hye Ji; Shin, Nara; Choi, Kwang Nam
- 발행일
- 2016-05
- 유형
- Proceedings Paper
- 저널명
- FIRST INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION
- 권
- 0011
- 언어
- ENG
- 출판사
- SPIE-INT SOC OPTICAL ENGINEERING
- 발행국가
- 미국
- ISSN
- E 1996-756X
P 0277-786X